diff --git a/README.md b/README.md
index f0c08b9..d7a5aed 100644
--- a/README.md
+++ b/README.md
@@ -72,7 +72,7 @@ and has [API documentation available online](https://libensemble.readthedocs.io/
 6. #### Distributed Consensus-based Optimization Methods
    *Generator and Allocator Functions*
 
-   Four generator functions, a matching allocator function, a consensus-subroutines module, and tests for each. 
+   Four generator functions, a matching allocator function, a consensus-subroutines module, a handful of test simulation functions, and tests for each. 
    All created by Caleb Ju at ANL as Given's associate Summer 2021. See the docstrings in each of the modules for more information.
 
    Last included in libEnsemble v0.9.3. See [here](https://github.com/Libensemble/libensemble/tree/v0.9.3/libensemble/gen_funcs).
\ No newline at end of file
diff --git a/consensus/persistent_independent_optimize.py b/consensus/gens/persistent_independent_optimize.py
similarity index 100%
rename from consensus/persistent_independent_optimize.py
rename to consensus/gens/persistent_independent_optimize.py
diff --git a/consensus/persistent_n_agent.py b/consensus/gens/persistent_n_agent.py
similarity index 100%
rename from consensus/persistent_n_agent.py
rename to consensus/gens/persistent_n_agent.py
diff --git a/consensus/persistent_pds.py b/consensus/gens/persistent_pds.py
similarity index 100%
rename from consensus/persistent_pds.py
rename to consensus/gens/persistent_pds.py
diff --git a/consensus/persistent_prox_slide.py b/consensus/gens/persistent_prox_slide.py
similarity index 100%
rename from consensus/persistent_prox_slide.py
rename to consensus/gens/persistent_prox_slide.py
diff --git a/consensus/sims/alt_rosenbrock.py b/consensus/sims/alt_rosenbrock.py
new file mode 100644
index 0000000..86d29fe
--- /dev/null
+++ b/consensus/sims/alt_rosenbrock.py
@@ -0,0 +1,51 @@
+import numpy as np
+
+const = 1500
+
+
+def EvaluateFunction(x, component=np.nan):
+    """
+    Evaluates the chained Rosenbrock function
+    """
+
+    assert not np.isnan(component), "Must give a component"
+
+    i = component
+    x1 = x[i]
+    x2 = x[i + 1]
+    f = 100 * (x1**2 - x2) ** 2 + (x1 - 1) ** 2
+
+    return f
+
+
+def EvaluateJacobian(x, component=np.nan):
+    """
+    Evaluates the chained Rosenbrock Jacobian
+    """
+
+    df = np.zeros(len(x), dtype=float)
+
+    assert not np.isnan(component), "Must give a component"
+
+    i = component
+    x1 = x[i]
+    x2 = x[i + 1]
+
+    df[i] = 400 * x1 * (x1**2 - x2) + 2 * (x1 - 1)
+    df[i + 1] = -200 * (x1**2 - x2)
+
+    return 1.0 / const * df
+
+
+def alt_rosenbrock_eval(H, persis_info, sim_specs, _):
+    batch = len(H["x"])
+    H_o = np.zeros(batch, dtype=sim_specs["out"])
+
+    for i, x in enumerate(H["x"]):
+        obj_component = H["obj_component"][i]  # which f_i
+        if H[i]["get_grad"]:
+            H_o["gradf_i"][i] = EvaluateJacobian(x, obj_component)
+        else:
+            H_o["f_i"][i] = EvaluateFunction(x, obj_component)
+
+    return H_o, persis_info
diff --git a/consensus/sims/geomedian.py b/consensus/sims/geomedian.py
new file mode 100644
index 0000000..182142b
--- /dev/null
+++ b/consensus/sims/geomedian.py
@@ -0,0 +1,48 @@
+__all__ = ["geomedian_eval"]
+import numpy as np
+import numpy.linalg as la
+
+
+def EvaluateFunction(x, component, B):
+    """
+    Evaluates the sum of squares-variant chwirut function
+    """
+    m = B.shape[0]
+    assert B.shape[1] == len(x)
+    assert 0 <= component <= m - 1
+    i = component
+    b_i = B[i]
+
+    f_i = 1.0 / m * la.norm(x - b_i)
+    return f_i
+
+
+def EvaluateJacobian(x, component, B):
+    """
+    Evaluates the sum of squares-variant chwirut Jacobian
+    """
+    m = B.shape[0]
+    assert B.shape[1] == len(x)
+    assert 0 <= component <= m - 1
+    i = component
+    b_i = B[i]
+
+    df_i = 1.0 / m * (x - b_i) / la.norm(x - b_i)
+    return df_i
+
+
+def geomedian_eval(H, persis_info, sim_specs, _):
+    B = persis_info["params"]["B"]
+
+    num_xs = len(H["x"])  # b==1 always?
+    H_o = np.zeros(num_xs, dtype=sim_specs["out"])
+
+    for k, x in enumerate(H["x"]):
+        i = H[k]["obj_component"]  # f_i
+
+        if H[k]["get_grad"]:
+            H_o["gradf_i"][k] = EvaluateJacobian(x, i, B)
+        else:
+            H_o["f_i"][k] = EvaluateFunction(x, i, B)
+
+    return H_o, persis_info
diff --git a/consensus/sims/linear_regression.py b/consensus/sims/linear_regression.py
new file mode 100644
index 0000000..3ed9fb8
--- /dev/null
+++ b/consensus/sims/linear_regression.py
@@ -0,0 +1,70 @@
+import numpy as np
+
+
+def EvaluateFunction(theta, component, X, y, c, reg):
+    """
+    Evaluates linear regression with l2 regularization
+    """
+    i = component
+    m = len(y)
+
+    y_i = y[i]
+    X_i = X[:, i]
+    XT_theta = np.dot(X_i, theta)
+
+    f_i = (1 / m) * (np.dot(y_i, y_i) - 2 * y_i * XT_theta + XT_theta**2)
+
+    assert reg == "l2", "Only l2 regularization allowed"
+    # if reg is None:
+    #     reg_val = 0
+    # elif reg == 'l1':
+    #     reg_val = (c/m) * np.sum(np.abs(theta))
+    reg_val = (c / m) * np.dot(theta, theta)
+
+    return f_i + reg_val
+
+
+def EvaluateJacobian(theta, component, X, y, c, reg):
+    """
+    Evaluates linear regression with l2 regularization
+    """
+
+    i = component
+    m = len(y)
+
+    y_i = y[i]
+    X_i = X[:, i]
+
+    df_i = (2 / m) * (-y_i + np.dot(X_i, theta)) * X_i
+
+    assert reg == "l2", "Only l2 regularization allowed"
+
+    # if reg is None:
+    #     reg_val = 0
+    # elif reg == 'l1':
+    #     reg_val = (c/m) * np.sign(theta)
+    reg_val = (2 * c / m) * theta
+
+    return df_i + reg_val
+
+
+def linear_regression_eval(H, persis_info, sim_specs, _):
+    X = persis_info["params"]["X"]
+    y = persis_info["params"]["y"]
+    c = persis_info["params"]["c"]
+    reg = persis_info["params"].get("reg", None)
+
+    assert (reg is None) or (reg == "l1") or (reg == "l2"), f"Incompatible regularization {reg}"
+
+    batch = len(H["x"])
+    H_o = np.zeros(batch, dtype=sim_specs["out"])
+
+    for i, x in enumerate(H["x"]):
+        obj_component = H["obj_component"][i]  # which f_i
+
+        if H[i]["get_grad"]:
+            H_o["gradf_i"][i] = EvaluateJacobian(x, obj_component, X, y, c, reg)
+        else:
+            H_o["f_i"][i] = EvaluateFunction(x, obj_component, X, y, c, reg)
+
+    return H_o, persis_info
diff --git a/consensus/sims/logistic_regression.py b/consensus/sims/logistic_regression.py
new file mode 100644
index 0000000..db91294
--- /dev/null
+++ b/consensus/sims/logistic_regression.py
@@ -0,0 +1,72 @@
+import numpy as np
+
+
+def EvaluateFunction(theta, component, X, y, c, reg):
+    """
+    Evaluates linear regression with l2 regularization
+    """
+    i = component
+    m = len(y)
+
+    y_i = y[i]
+    X_i = X[:, i]
+
+    base = np.exp(-y_i * np.dot(X_i, theta))
+
+    f_i = (1 / m) * np.log(1 + base)
+
+    assert reg == "l2", "Only l2 regularization allowed"
+    # if reg is None:
+    #     reg_val = 0
+    # elif reg == 'l1':
+    #     reg_val = (c/m) * np.sum(np.abs(theta))
+    reg_val = (c / m) * np.dot(theta, theta)
+
+    return f_i + reg_val
+
+
+def EvaluateJacobian(theta, component, X, y, c, reg):
+    """
+    Evaluates linear regression with l2 regularization
+    """
+
+    i = component
+    m = len(y)
+
+    y_i = y[i]
+    X_i = X[:, i]
+
+    base = np.exp(-y_i * np.dot(X_i, theta))
+
+    df_i = (1 / m) * (-y_i * base) / (1 + base) * X_i
+
+    assert reg == "l2", "Only l2 regularization allowed"
+    # if reg is None:
+    #     reg_val = 0
+    # elif reg == 'l1':
+    #     reg_val = (c/m) * np.sign(theta)
+    reg_val = (2 * c / m) * theta
+
+    return df_i + reg_val
+
+
+def logistic_regression_eval(H, persis_info, sim_specs, _):
+    X = persis_info["params"]["X"]
+    y = persis_info["params"]["y"]
+    c = persis_info["params"]["c"]
+    reg = persis_info["params"].get("reg", None)
+
+    assert (reg is None) or (reg == "l1") or (reg == "l2"), f"Incompatible regularization {reg}"
+
+    batch = len(H["x"])
+    H_o = np.zeros(batch, dtype=sim_specs["out"])
+
+    for i, x in enumerate(H["x"]):
+        obj_component = H["obj_component"][i]  # which f_i
+
+        if H[i]["get_grad"]:
+            H_o["gradf_i"][i] = EvaluateJacobian(x, obj_component, X, y, c, reg)
+        else:
+            H_o["f_i"][i] = EvaluateFunction(x, obj_component, X, y, c, reg)
+
+    return H_o, persis_info
diff --git a/consensus/sims/nesterov_quadratic.py b/consensus/sims/nesterov_quadratic.py
new file mode 100644
index 0000000..85c1187
--- /dev/null
+++ b/consensus/sims/nesterov_quadratic.py
@@ -0,0 +1,65 @@
+import numpy as np
+
+
+def EvaluateFunction(x, component):
+    """
+    Evaluates the chained Rosenbrock function
+    """
+    n = len(x)
+
+    i = component
+    assert 0 <= i <= n
+
+    if i == 0:
+        x_1 = x[i]
+        f_i = 0.5 * x_1**2 - x_1
+    elif i == n:
+        x_n = x[-1]
+        f_i = 0.5 * x_n**2
+    else:
+        x_1 = x[i - 1]
+        x_2 = x[i]
+        f_i = 0.5 * (x_2 - x_1) ** 2
+
+    return f_i
+
+
+def EvaluateJacobian(x, component=np.nan):
+    """
+    Evaluates the chained Rosenbrock Jacobian
+    """
+    n = len(x)
+    df = np.zeros(n, dtype=float)
+
+    i = component
+    assert 0 <= i <= n
+
+    if i == 0:
+        x_1 = x[i]
+        df[0] = x_1 - 1
+    elif i == n:
+        x_n = x[-1]
+        df[-1] = x_n
+    else:
+        x_1 = x[i - 1]
+        x_2 = x[i]
+
+        df[i - 1] = x_1 - x_2
+        df[i] = x_2 - x_1
+
+    return df
+
+
+def nesterov_quadratic_eval(H, persis_info, sim_specs, _):
+    batch = len(H["x"])
+    H_o = np.zeros(batch, dtype=sim_specs["out"])
+
+    for i, x in enumerate(H["x"]):
+        obj_component = H["obj_component"][i]  # which f_i
+
+        if H[i]["get_grad"]:
+            H_o["gradf_i"][i] = EvaluateJacobian(x, obj_component)
+        else:
+            H_o["f_i"][i] = EvaluateFunction(x, obj_component)
+
+    return H_o, persis_info
diff --git a/consensus/sims/svm.py b/consensus/sims/svm.py
new file mode 100644
index 0000000..39d08bf
--- /dev/null
+++ b/consensus/sims/svm.py
@@ -0,0 +1,77 @@
+import numpy as np
+
+
+def EvaluateFunction(theta, component, X, b, c, reg):
+    """
+    Evaluates svm with l1 regularization
+    """
+    i = component
+    m = len(b)
+
+    b_i = b[i]
+    X_i = X[:, i]
+
+    f_i = max(0, 1 - b_i * np.dot(X_i, theta))
+
+    assert reg == "l1", "Only l1 regularization allowed"
+
+    # if reg is None:
+    #     reg_val = 0
+    # elif reg == 'l1':
+    reg_val = (c / m) * np.sum(np.abs(theta))
+    # else:
+    #     reg_val = (c/m) * np.dot(theta, theta)
+
+    return f_i + reg_val
+
+
+def EvaluateJacobian(theta, component, X, b, c, reg):
+    """
+    Evaluates svm with l1 regularization
+    """
+
+    i = component
+    m = len(b)
+
+    b_i = b[i]
+    X_i = X[:, i]
+
+    score = b_i * np.dot(X_i, theta)
+
+    if score >= 1:
+        df_i = np.zeros(len(theta))
+    else:
+        df_i = -b_i * X_i
+
+    assert reg == "l1", "Only l1 regularization allowed"
+
+    # if reg is None:
+    #     reg_val = 0
+    # elif reg == 'l1':
+    reg_val = (c / m) * np.sign(theta)
+    # else:
+    #     reg_val = (2*c/m) * theta
+
+    return df_i + reg_val
+
+
+def svm_eval(H, persis_info, sim_specs, _):
+    X = persis_info["params"]["X"]
+    b = persis_info["params"]["b"]
+    c = persis_info["params"]["c"]
+    reg = persis_info["params"].get("reg", None)
+
+    assert (reg is None) or (reg == "l1") or (reg == "l2"), f"Incompatible regularization {reg}"
+
+    batch = len(H["x"])
+    H_o = np.zeros(batch, dtype=sim_specs["out"])
+
+    for i, x in enumerate(H["x"]):
+        obj_component = H["obj_component"][i]  # which f_i
+
+        if H[i]["get_grad"]:
+            H_o["gradf_i"][i] = EvaluateJacobian(x, obj_component, X, b, c, reg)
+        else:
+            H_o["f_i"][i] = EvaluateFunction(x, obj_component, X, b, c, reg)
+
+    return H_o, persis_info
diff --git a/consensus/test_persistent_independent.py b/consensus/test_persistent_independent.py
index c866905..7bcb42d 100644
--- a/consensus/test_persistent_independent.py
+++ b/consensus/test_persistent_independent.py
@@ -23,9 +23,9 @@
 import scipy.sparse as spp
 
 from libensemble.alloc_funcs.start_persistent_consensus import start_consensus_persistent_gens as alloc_f
-from libensemble.gen_funcs.persistent_independent_optimize import independent_optimize as gen_f
+from gens.persistent_independent_optimize import independent_optimize as gen_f
 from libensemble.libE import libE
-from libensemble.sim_funcs.rosenbrock import rosenbrock_eval as sim_f
+from sims.rosenbrock import rosenbrock_eval as sim_f
 from libensemble.tools import add_unique_random_streams, parse_args
 from consensus_subroutines import get_k_reach_chain_matrix
 
diff --git a/consensus/test_persistent_n_agent.py b/consensus/test_persistent_n_agent.py
index e1dd48d..310a5f6 100644
--- a/consensus/test_persistent_n_agent.py
+++ b/consensus/test_persistent_n_agent.py
@@ -36,13 +36,13 @@
 import scipy.sparse as spp
 
 from libensemble.alloc_funcs.start_persistent_consensus import start_consensus_persistent_gens as alloc_f
-from libensemble.gen_funcs.persistent_n_agent import n_agent as gen_f
+from gens.persistent_n_agent import n_agent as gen_f
 from libensemble.libE import libE
-from libensemble.sim_funcs.alt_rosenbrock import alt_rosenbrock_eval
-from libensemble.sim_funcs.linear_regression import linear_regression_eval
-from libensemble.sim_funcs.logistic_regression import logistic_regression_eval
-from libensemble.sim_funcs.nesterov_quadratic import nesterov_quadratic_eval
-from libensemble.sim_funcs.rosenbrock import rosenbrock_eval
+from sims.alt_rosenbrock import alt_rosenbrock_eval
+from sims.linear_regression import linear_regression_eval
+from sims.logistic_regression import logistic_regression_eval
+from sims.nesterov_quadratic import nesterov_quadratic_eval
+from sims.rosenbrock import rosenbrock_eval
 from libensemble.tools import add_unique_random_streams, parse_args
 from consensus_subroutines import get_doubly_stochastic, get_k_reach_chain_matrix, log_opt, regls_opt
 
diff --git a/consensus/test_persistent_pds.py b/consensus/test_persistent_pds.py
index 31fe073..f853385 100644
--- a/consensus/test_persistent_pds.py
+++ b/consensus/test_persistent_pds.py
@@ -36,13 +36,13 @@
 import scipy.sparse as spp
 
 from libensemble.alloc_funcs.start_persistent_consensus import start_consensus_persistent_gens as alloc_f
-from libensemble.gen_funcs.persistent_pds import opt_slide as gen_f
+from gens.persistent_pds import opt_slide as gen_f
 from libensemble.libE import libE
-from libensemble.sim_funcs.alt_rosenbrock import alt_rosenbrock_eval
-from libensemble.sim_funcs.linear_regression import linear_regression_eval
-from libensemble.sim_funcs.logistic_regression import logistic_regression_eval
-from libensemble.sim_funcs.nesterov_quadratic import nesterov_quadratic_eval
-from libensemble.sim_funcs.rosenbrock import rosenbrock_eval
+from sims.alt_rosenbrock import alt_rosenbrock_eval
+from sims.linear_regression import linear_regression_eval
+from sims.logistic_regression import logistic_regression_eval
+from sims.nesterov_quadratic import nesterov_quadratic_eval
+from sims.rosenbrock import rosenbrock_eval
 from libensemble.tools import add_unique_random_streams, parse_args
 from consensus_subroutines import get_k_reach_chain_matrix, log_opt, regls_opt
 
diff --git a/consensus/test_persistent_prox_slide.py b/consensus/test_persistent_prox_slide.py
index cb59156..35f3b07 100644
--- a/consensus/test_persistent_prox_slide.py
+++ b/consensus/test_persistent_prox_slide.py
@@ -30,10 +30,10 @@
 import scipy.sparse as spp
 
 from libensemble.alloc_funcs.start_persistent_consensus import start_consensus_persistent_gens as alloc_f
-from libensemble.gen_funcs.persistent_prox_slide import opt_slide as gen_f
+from gens.persistent_prox_slide import opt_slide as gen_f
 from libensemble.libE import libE
-from libensemble.sim_funcs.geomedian import geomedian_eval
-from libensemble.sim_funcs.svm import svm_eval
+from sims.geomedian import geomedian_eval
+from sims.svm import svm_eval
 from libensemble.tools import add_unique_random_streams, parse_args
 from consensus_subroutines import get_k_reach_chain_matrix, gm_opt, readin_csv, svm_opt
 
diff --git a/setup.py b/setup.py
index 6b4a958..2b80d68 100644
--- a/setup.py
+++ b/setup.py
@@ -25,7 +25,6 @@
         'Operating System :: Unix',
         'Operating System :: MacOS',
         'Programming Language :: Python :: 3',
-        'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8',
         'Programming Language :: Python :: 3.9',
         'Programming Language :: Python :: 3.10',